Effects of technical innovation on market value of the U.S. semiconductor industry

Lou, Chen-Chi; Lee, Tsung-Pei; Gong, Shang-Chi; Lin, Shu-Ling

doi:10.1016/j.techfore.2010.04.016

Cited by 9 publications

(8 citation statements)

References 38 publications

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“…Taiwan Semiconductor Manufacturing Company (TSMC) and United Microelectronics Corporation (UMC) are the first and second in worldwide ranking, with a combined market share exceeding 50%. IC assembly industry total product value is the first of the worldwide [13]. Table 1 shows the historical data series of the IC assembly industry Revenue of Taiwan from 2007 Q1 to 2010 Q3, but fluctuating randomly.…”

Section: Results and Analysesmentioning

confidence: 99%

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

2013

Mathematical Problems in Engineering

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This study utilizes the black swan theorem to discuss how to face the lack of historical data and outliers. They may cause huge influences which make it impossible for people to predict the economy from their knowledge or experiences. Meanwhile, they cause the general dilemma of which prediction tool to be used which is also considered in this study. For the reason above, this study uses 2009 Q1 to 2010 Q4 quarterly revenue trend of Taiwan’s semiconductor packaging and testing industry under the global financial turmoil as basis and the grey prediction method to deal with nonlinear problems and small data. Under the lack of information and economic drastic changes, this study applies Markov model to predict the industry revenues of GM(1,1) and DGM(1,1) results. The results show that the accuracy of 2010 Q1–Q3 is 88.37%, 90.27%, sand 91.13%, respectively. Besides, they are better than the results of GM(1,1) and DGM(1,1) which are 86.51%, 77.35%, 75.46% and 73.77%, 74.25%, 59.72%. The results show that the prediction ability of the grey prediction with Markov model is better than traditional GM(1,1) and DGM(1,1) sfacing the changes of financial crisis. The results also prove that the grey-Markov chain prediction can be the perfect criterion for decision-makers judgment even when the environment has undergone drastic changes which bring the impact of unpredictable conditions.

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Section: Results and Analysesmentioning

confidence: 99%

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

2013

Mathematical Problems in Engineering

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“…Model (7) replaces +d x− i by −d x+ i in the formulation. No other difference can be found between the two models ( 5) and (7). The level of unified efficiency (UEN(k) I c ) of the k th DMU under managerial disposability becomes as follows:…”

Section: Two Formulations Under Constant Rts and Dtsmentioning

confidence: 99%

“…To do that, they conducted not only interviews but also patent analyses. Furthermore, the references [6,7] tested alternative variables (e.g., patent quality and average process technology) to the number of patents in linking semiconductor firms' technological innovation to corporate profitability or stock market value.…”

Section: Technological Innovation and Environmental Sustainability In...mentioning

confidence: 99%

Performance Assessment of the Semiconductor Industry: Measured by DEA Environmental Assessment

Sueyoshi,

Ryu

2020

Energies

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This study measures the unified (i.e., operational and environmental) performance of semiconductor firms in the world by using Data Envelopment Analysis (DEA) environmental assessment. With its promising and expanding electronic applications, many industrial nations have supported the semiconductor industry under their strategic plans, and numerous firms are involved in the global value chain. Drawing on the proposed DEA-based environmental (sustainability) assessment, which uses two disposability criteria (i.e., natural and managerial), this study first compute the unified efficiency scores of semiconductor firms. Then, this study explores how corporate age, business model, and location influence the efficiency scores by employing Tobit regressions and t-tests. The empirical implications obtained from this research indicate that overall, the semiconductor firms look for their economic achievements but are not paying enough attention to environmental sustainability. Corporate age and business model are statistically related with their operational performance measures whereas corporate location is related with their environmental ones.

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“…In this study, we used the stock price model (Ohlson, 1995) to assess investor reaction to R&D investment because stock price is usually an accurate reflection of investor sentiment about a firm (Lou et al, 2010). Thus, by examining the relationship between R&D investment and stock price, we can infer how investors respond to R&D investment.…”

Section: The Modelmentioning

confidence: 99%

Investor reaction to R&D investment

Wang

2016

IJIS

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Purpose This paper aims to examine the impact of research and development (R&D) investment on firms’ stock price from the perspective of investors. Design/methodology/approach Building on signaling theory, the authors propose that R&D investment sends important signals to the investment community regarding future growth, which in turn impacts investor reaction to such investment. Findings Using a sample of listed pharmaceutical firms in China from 2007 to 2011, the authors find that R&D investment has a positive effect on firms’ stock price, indicating that investors have a positive reaction to R&D investment signals. Further, the authors find that the signaling role of new product announcements mediates this relationship between R&D investment and investor reaction. Originality/value The authors also find that the signaling role of development capacity (DC) has a moderating effect on the relationship between innovation activities (i.e. R&D investment and new product announcements) and investor reaction, such that DC strengthens the positive effect of R&D and new product announcements on investors.

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Effects of technical innovation on market value of the U.S. semiconductor industry

Cited by 9 publications

References 38 publications

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

Performance Assessment of the Semiconductor Industry: Measured by DEA Environmental Assessment

Investor reaction to R&D investment

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